Ticket Resolution Time Summary and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, analyze the ticket resolution time summary through transformations, and move the cleaned data into the destination database using Bold Data Hub. Follow the step-by-step process below.

Sample Data Source:
Sample CSC Data

Step-by-Step Process in Bold Data Hub

Step 1: Open Bold Data Hub

• Click on the Bold Data Hub.

Step 2: Create a New Pipeline

• Click Add Pipeline in the left-side panel.
• Enter the pipeline name and click the tick icon.

Step 3: Choose the Connector

• Select the newly created pipeline and opt for the CSV connector. You can either double-click or click on the Add Template option to include a template.

Step 4: Upload Your CSV File

• Click the “Upload File” button to select and upload your CSV file.

Step 5: Set the Properties

• Copy the file path and paste it into the filePath property field.

Step 6: Save and Choose the Destination

• Click Save, choose the destination, and confirm by clicking the Yes button.

Note: On-Demand Refresh will be triggered when the pipeline is saved. If needed, the pipeline can be scheduled in the Schedules tab.

Step 7: View Logs and Outputs

• Click the pipeline name in the left-side panel and switch to the Logs tab to view logs.

Step 8: Apply Transformations

• Go to the Transform tab and click Add Table.

• Enter the table name to create a transform table for customer satisfaction summary.

Note: The data will initially be transferred to the DuckDB database within the designated {pipeline_name} schema before undergoing transformation for integration into the target databases. As an illustration, in the case of a pipeline named “customer_service_data”, the data will be relocated to the customer_service_data table schema.

Learn more about transformation here

Ticket Resolution Time Summary

Overview

Analyzing ticket resolution times per service category helps identify efficiency trends and potential areas for improvement. We calculate the average, minimum, and maximum resolution times for each ticket category.

Approach

We aggregate resolution time statistics for resolved tickets:

• Average Resolution Time → Mean time taken to resolve tickets
• Minimum Resolution Time → Fastest resolution recorded
• Maximum Resolution Time → Longest resolution duration

SQL Query for Ticket Resolution Time Summary

SELECT 
    Ticket_Category, 
    AVG("Resolution_Time (hrs)") AS Avg_Resolution_Time, 
    MIN("Resolution_Time (hrs)") AS Min_Resolution_Time, 
    MAX("Resolution_Time (hrs)") AS Max_Resolution_Time 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved' 
GROUP BY Ticket_Category 
ORDER BY Ticket_Category;